The Big Data Ripple Effect on Retailers

As consumerism remains a favorite preoccupation in the global community, one of the popular questions that pops up in conversation threads and discussion forums is What, if any, has been the impact of big data analytics on the retail industry? Todays value-conscious consumers have traversed beyond price and feature comparison charts, and now they try to make holistic, qualitative evaluations of brands or products to make value judgments on their purchases. In an increasingly sophisticated consumer environment, the current buyers want to know how big data started in retail, how it evolved, and how it influences consumers to make a purchase.

The objective of this post is not to highlight the implementation of a single, big data strategy in a particular retail business, but rather to review the common big data technology trends that have impacted the global retail business over the years. Specific big data implementations are tackled in case-study posts.

The history of big data in retail

In the 90s, very few large businesses like Walmart took advantage of big data analytics to control excessive inventory and associated storage costs. Walmart, being a warehouse retailer, went a step further to drastically reduce inventory based on the insights derived from their analytics efforts, and passed on the financial gains to the consumer by way of additional savings or cost reduction on goods. The warehouses were probably the earliest adopters of big data as they proactively utilized their market intelligence to improve operational efficiency and cost reduction.

At the beginning of the twenty-first century, online behemoths like Amazon started leveraging recommenders to suggest additional purchase items to consumersbased on their previous purchase patterns. In some cases, Amazon even took advantage of real-time analytics to offer on-the-spot savings for specific purchase items. Companies like Walmart and Amazon demonstrate early adoptions of big data analytics for both brick and mortar and online shops.

Big data adoption in the current retail industry

In the current market, retailers big, medium, or small have suddenly turned to big data analyticssome without knowing whats in store for the future! Big data analytics may have matured in the retail industry in specific parts of the world like the US, but in developing or underdeveloped countries, the power of big data has still not been realized by average retailers. Going by the recent trends and patterns of big data adoption and technology implementation in the global retail industry, one finds definite, common patterns to have transformed the way in which retail businesses operate.

Big data analytics: Back-end activity in retail

A large number of businesses have embraced Hadoop with data capturing and storage for cost-effective storage of terabytes of big data into a central repository for further analytics.

Unstructured data is sometimes moved to relational databases, transformed, and then analyzed along with structured data for deriving comparative insights and intelligence.

The Cloud platform has suddenly brought the power of high-end data capture, storage, and analytics to the medium or small business owners without any investment overhead. In fact, cloud has enabled the low-budget retailers to take advantage of business data from various consumer touch points in their businesses.

A platform like Cloud has also facilitated the availability of high-end computational or processing software and services that would have ordinarily been out of the reach of an average retail business person.

Technology sharing has been made possible because of open-source projects, so that retail business owners do not have to invest time and money in locating highly focused solutions for their needs. Open-source, big data technologies and tools are widely distributed and shared amongst the big data community.

The current big data practice has evolved into combined analytics of structured and unstructured data in retail, such as text, photos, audio clips, videos, or even colors, or emoticons for improving the consumer experience.

Big data analytics: Front-end activity in retail

Based on real-time monitoring of stores or online showrooms, business managers can dynamically alter the price of products based on sales or demand.

More and more retail business owners are studying videos and interactive sessions of daily operations to make better decisions about their business practices such as store layouts, location and display of inventory, spot sales, announcements of prizes, rewards, or coupons.

Front end activities like customer service logs, technical support calls, recorded customer visits, or social media accounts generate a huge amount of data that need in-depth analysis. The modern big data technologies and tools have enables all types of retail businesses to tap into multi-structured data hived from different sources to make better business decisions or improve customer experience.

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